MétaCan
Menu
Back to cohort

Breastfeeding Triplets: The At‐Home Experience

2000· review· en· W2097589955 on OpenAlexaff
Linda G. Leonard

Bibliographic record

VenuePublic Health Nursing · 2000
Typereview
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBreastfeedingWeaningMedicineBreast feedingBreast milkNursingPublic healthDuration (music)Diversity (politics)Quality (philosophy)Social supportFamily medicineEnvironmental healthPediatricsPsychologySocial psychology

Abstract

fetched live from OpenAlex

The unprecedented rise in higher-order multiple (HOM) births and the subsequent increase in parents who want to breastfeed their babies presents community health nurses (CHNs) with complex challenges. The extraordinary diversity of the triplet breastfeeding experience once all infants are settled at home is illustrated through findings from a survey of nine mothers of triplets. Parents revealed how they managed the feedings over the multiples' first year: type of feedings (breast, expressed breast milk [EBM], formula); scheduled and demand feedings; adequacy of milk supply; frequency and duration of feedings; consecutive and simultaneous feedings; nighttime with three; weaning; effects of breastfeeding on their bodies and well-being; challenges and stresses; and spousal, family, and health professional attitudes and support. A number of strategies that CHNs can utilize are suggested. These include working with individual families as well as forming partnerships with parents of multiples' support groups, multiple birth associations, interested health professionals, and the community sector. It is through these actions that the accessibility, coordination, and quality of health, multiple birth education, and social support services can be strengthened and healthy public policies implemented which address the unique and enormous demands experienced by HOMs' families.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.164
GPT teacher head0.438
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2000
Admission routes1
Has abstractyes

Explore more

Same venuePublic Health NursingSame topicBreastfeeding Practices and InfluencesFrench-language works237,207